Geodesic
Study of SEIRD adaptive-compartmental model of COVID-19 epidemic spread in Russian Federation using optimization methods
Matematičeskaâ biologiâ i bioinformatika, Tome 16 (2021) no. 1, pp. 136-151.

Voir la notice de l'article provenant de la source Math-Net.Ru

A systemic approach to the study of a new multi-parameter model of the COVID-19 pandemic spread is proposed, which has the ultimate goal of optimizing the manage parameters of the model. The approach consists of two main parts: 1) an adaptive-compartmental model of the epidemic spread, which is a generalization of the classical SEIR model, and 2) a module for adjusting the parameters of this model from the epidemic data using intelligent optimization methods. Data for testing the proposed approach using the pandemic spread in some regions of the Russian Federation were collected on a daily basis from open sources during the first 130 days of the epidemic, starting in March 2020. For this, a so-called "data farm" was developed and implemented on a local server (an automated system for collecting, storing and preprocessing data from heterogeneous sources, which, in combination with optimization methods, allows most accurately tune the parameters of the model, thus turning it into an intelligent system to support management decisions). Among all model parameters used, the most important are: the rate of infection transmission, the government actions and the population reaction. 
@article{MBB_2021_16_1_a3,
     author = {S. P. Levashkin and S. N. Agapov and O. I. Zakharova and K. N. Ivanov and E. S. Kuzmina and V. A. Sokolovsky and A. S. Monasova and A. V. Vorobiev and D. N. Apeshin},
     title = {Study of {SEIRD} adaptive-compartmental model of {COVID-19} epidemic spread in {Russian} {Federation} using optimization methods},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {136--151},
     publisher = {mathdoc},
     volume = {16},
     number = {1},
     year = {2021},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MBB_2021_16_1_a3/}
}
TY  - JOUR
AU  - S. P. Levashkin
AU  - S. N. Agapov
AU  - O. I. Zakharova
AU  - K. N. Ivanov
AU  - E. S. Kuzmina
AU  - V. A. Sokolovsky
AU  - A. S. Monasova
AU  - A. V. Vorobiev
AU  - D. N. Apeshin
TI  - Study of SEIRD adaptive-compartmental model of COVID-19 epidemic spread in Russian Federation using optimization methods
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2021
SP  - 136
EP  - 151
VL  - 16
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2021_16_1_a3/
LA  - ru
ID  - MBB_2021_16_1_a3
ER  - 
%0 Journal Article
%A S. P. Levashkin
%A S. N. Agapov
%A O. I. Zakharova
%A K. N. Ivanov
%A E. S. Kuzmina
%A V. A. Sokolovsky
%A A. S. Monasova
%A A. V. Vorobiev
%A D. N. Apeshin
%T Study of SEIRD adaptive-compartmental model of COVID-19 epidemic spread in Russian Federation using optimization methods
%J Matematičeskaâ biologiâ i bioinformatika
%D 2021
%P 136-151
%V 16
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2021_16_1_a3/
%G ru
%F MBB_2021_16_1_a3
S. P. Levashkin; S. N. Agapov; O. I. Zakharova; K. N. Ivanov; E. S. Kuzmina; V. A. Sokolovsky; A. S. Monasova; A. V. Vorobiev; D. N. Apeshin. Study of SEIRD adaptive-compartmental model of COVID-19 epidemic spread in Russian Federation using optimization methods. Matematičeskaâ biologiâ i bioinformatika, Tome 16 (2021) no. 1, pp. 136-151. http://geodesic.mathdoc.fr/item/MBB_2021_16_1_a3/

[1] Bernoulli D., “Essai d'une nouvelle analyse de la mortalité causée par la petite vérole et des avantages de l'inoculum pour la prévenir”, Mém. Math. Phys. Acad. Roy. Sci. Paris, 1760, 1–45

[2] Farr W., Progress of epidemics. 2d Report of the regist, General of England and Wales, London, 1840

[3] Enko P.L., “O khode epidemii nekotorykh zaraznykh boleznei”, Vrach, 4, SPb, 1889, 6–48

[4] Ross R., Hudson H.P., “An application of the theory of probabilities to the study of a priori pathometry”, Proc. R. Soc. Lond, A 93 (1916–1917), 212–240 | MR

[5] Beili N., Matematika v biologii i meditsine, MIR, M., 1970, 326 pp.

[6] Baroyan O.V., Rvachev L.A., Matematika i epidemiologiya, Znanie, M., 1977, 63 pp.

[7] Boev B.V., “Sovremennye etapy matematicheskogo modelirovaniya protsessov razvitiya i rasprostraneniya infektsionnykh zabolevanii”, Epidemiologicheskaya kibernetika: modeli, informatsiya, eksperimenty, 1991, 6–13

[8] Anderson R.M., May R.M., Infectious Diseases of Humans. Dynamics and Control, Oxford University Press, Oxford, 1991

[9] Macdonald G., “The measurement of malaria transmission”, Proc. R. Soc. Med., 48:4 (1955), 295–302

[10] Hoppensteadt F., “An age dependent epidemic model”, Journal of the Franklin Institute, 1974, 325–333 | DOI | MR | Zbl

[11] Gupur G., Li Xue-Zhi, Zhu Guang-Tian, “Threshold and Stability Results for an Age-Structured Epidemic Model”, Computers and Mathematics with Applications, 42 (2001), 883–907 | DOI | MR | Zbl

[12] Park T., Age-dependence in epidemic models of vector-borne infections, The University of Alabama, Huntsville, 2004 | MR

[13] Giordano G., Blanchini F., Bruno R., Colaneri P., Di Filippo A., Di Matteo A., Colaneri M., “Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy”, Nature Medicine, 26 (2020), 855–860 | DOI

[14] Bukin Yu.S., Dzhioev Yu.P., Bondaryuk A.N., Tkachev S.E., Zlobin V.I., Primenenie universalnoi matematicheskoi modeli epidemicheskogo protsessa «SRID» dlya prognoza razvitiya epidemii COVID-19 v gorode Moskva, preprint, 2020, 21 pp. | DOI | Zbl

[15] Derrode S., Gauchon R., Ponthus N., Rigotti C., Pothier C., Volpert V., Loisel S., Bertoglio J.-P., Roy P., Piecewise estimation of R0 by a simple SEIR model. Application to COVID-19 in French regions and departments until June 30, Université Lyon 1 - Claude Bernard, 2020 (accessed 14.05.2021) https://www.semanticscholar.org/paper/Piecewise-estimation-of-R0-by-a-simple-SEIR-model.-Derrode-Gauchon/7394add56a191e2fed3121c2d935d7f3f278320a#paper-header

[16] Kochańczyk M., Grabowski F., Lipniacki T., “Super-spreading events initiated the exponential growth phase of COVID-19 with $\mathcal{R}_0$ higher than initially estimated”, Biology, Medicine, Royal Society Open Science, 2020, 200786 | DOI

[17] Wang X., Tang T., Cao L., Aihara K., Guo Q., “Inferring key epidemiological parameters and transmission dinamics of COVID-19 based on a modifiend SEIR model”, Math. Model. Nat. Phenom., 15 (2020), 2020050, 74 pp. | DOI | MR

[18] Grigorieva E.V., Khailov E. N., Korobeinikov A., Optimal quarantine strategies for COVID-19 control models, 2020, 21 pp., arXiv: (accessed 14.05.2021) 2004.10614 [math.OC] | MR

[19] Zhou X., Ma X.-d., Hong N., Su L., Ma Y., He J., Jiang H., Liu C., Shan G., Zhu W., Zhang S., Long Y., Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model, Medicine, Geography. medRxiv, 2020 | DOI

[20] Lin Q., Zhao S., Gao D., Lou Y., Yang S., Musa S. S., Wang M.H., Cai Y., Wang W., Yang L., He D., “A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action”, International Journal of Infectious Diseases | DOI | MR

[21] Zamir M., Abdeljawad T., Nadeem F., Wahid A., Yousef A., “An optimal control analysis of a COVID-19 model Author links open overlay panel”, Alexandria Engineering Journal, 60:3 (2021), 2875–2884 | DOI

[22] Okhuese V.A., Mathematical predictions for COVID-19 as a global pandemic, medRxiv, 2020 | DOI

[23] Zeng Y., Guo X., Deng Q., Luo S., Zhang H., “Forecasting of COVID-19: spread with dynamic transmission rate”, Journal of Safety Science and Resilience, 1:2 (2020), 91–96 | DOI

[24] Tomchin D.A., Fradkov A.L., Prognozirovanie rasprostraneniya virusa COVID-19 v Rossii na osnove prostykh matematicheskikh modelei epidemii, IPMash RAN, Spb., 2020, 17 pp.

[25] Mamo D.K., “Model the transmission dynamics of COVID-19 propagation with public health intervention”, Results in Applied Mathematics, 7 (2020), 100123 | DOI | MR | Zbl

[26] Garba S. M., Lubuma J. M.-S., Tsanou B., “Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa”, Mathematical Biosciences, 328 (2020), 108441 | DOI | MR | Zbl

[27] Aghdaoui H., Tilioua M., Sooppy Nisar K., Khan I., “A Fractional Epidemic Model with Mittag-Leffler Kernel for COVID-19”, Mathematical Biology and Bioinformatics, 16:1 (2021), 39–56 | DOI | MR

[28] Kondratev M.A., “Metody prognozirovaniya i modeli rasprostraneniya zabolevanii”, Kompyuternye issledovaniya i modelirovanie, 5:5 (2013), 863–882

[29] Vasilev F.P., Metody optimizatsii, Faktorial Press, M., 2002

[30] Krivorotko O., Kabanikhin S., Zyatkov N., Prikhod'ko A., Prokhoshin N., Shishlenin M., “Mathematical modeling and prediction of COVID-19 in Moscow and Novosibirsk region”, Numerical Analysis and Applications, 13 (2020), 332–348 | DOI | MR

[31] He D., Dushoff J., Day T., Ma J., Earn D. J.-D., “Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales”, Proceedings of the Royal Society B: Biological Sciences, 2013 | DOI

[32] BlackBoxOptim.jl, (accessed 14.05.2021) https://github.com/robertfeldt/BlackBoxOptim.jl

[33] Samorodskaya I.V., “Problemy diagnostiki i ucheta zabolevaemosti COVID-19”, Medvestnik, 2020 (accessed 14.05.2021) https://medvestnik.ru/content/medarticles/Problemy-diagnostiki-i-ucheta-zabolevaemosti-COVID-19.html

[34] Klinicheskoe vedenie sluchaev COVID-19: vremennoe rukovodstvo, VOZ, 2020 (accessed 14.05.2021) https://apps.who.int/iris/bitstream/handle/10665/332196/WHO-2019-nCoV-clinical-2020.5-rus.pdf

[35] Anderson R.M., May R.M., “Directly transmitted infectious diseases: Control by vaccination”, Science, 1982, 1053–1060 | DOI | MR | Zbl

[36] Roberts M., Heesterbeek H., “Bluff your way in epidemic models”, Trends in Microbiology, 1:9 (1993), 343–348 | DOI

[37] Ferguson N.M., Keeling M.J., Edmunds W.J., Gani R., Grenfell B.T., Anderson R.M., Leach S., “Planning for smallpox outbreaks”, Nature, 425:6959 (2003), 681–685 | DOI

[38] Frost I., Craig J., Osena G., Hauck S., Kalanxhi E., Schueller E., Gatalo O., Yan-y Y., Tseng K., Lin G., Klein E., Modeling COVID-19 Transmission in Africa: Country-wise Projections of Total and Severe Infections Under Different Lockdown Scenarios, medRxiv, 2020 | DOI

[39] Bertozzi A.L., Franco E., Mohler G., Short M.B., Sledge D., “The challenges of modeling and forecasting the spread of COVID-19”, PNAS, 117:29 (2020), 16732–16738 | DOI | MR